Contrary to other medical imaging techniques (e.g., X-rays, magnetic resonance imaging, and computerized tomography), ultrasound imagery is currently considered to be a non-invasive, portable, non-expensive and safe (for the patient and operator) visualization medical tool for investigating biological tissues of a body. However, despite considerable advances in the technology of ultrasound imaging equipment over the last years, the primary limitation of this imaging modality remains its poor image quality (i.e. low signal-to-noise ratio, low resolution and contrast), and also the presence of artifacts due to the speckle noise effect that drastically deteriorates image quality and sometimes makes imperceptible clinically important details within these images (such as contours of anatomical structures).
In order to improve the quality of such ultrasound images, an image deconvolution/restoration procedure could be efficiently applied and, to this end, given a Point Spread Function (PSF) estimate, many deconvolution models exist [1]. The only requirement for such deconvolution algorithms consists, as a prerequisite first stage, of an estimation of the PSF of the underlying ultrasound imaging system. This problem of estimating the PSF and restoring is called a blind deconvolution process and an alternative approach to this above-mentioned estimation and deconvolution (disjoint) procedures consists of the simultaneous (generally iterative) estimation of the undegraded original image and the PSF (or its inverse) [2-5].
Amongst the first blind deconvolution strategy for which estimating the PSF estimation and the restoration process are two disjoint procedures, there is the PSF identification procedure based on frequency domain [6] zeros or the homomorphic filtering method which consists in low-pass filtering (also called liftering) in the complex cepstral domain (the cepstrum being defined by the inverse Fourier transform of the log of the spectrum). This low-pass filtering is commonly achieved either with an ideal low-pass filter [7, 8] or by hard or a soft shrinkage rule in the wavelet domain [9]. It is also worth mentioning the estimation approach by means of local polynomial approximation proposed by Adam and Michailovich [10], which can be viewed as a modification of homomorphic estimation by using wavelet bases instead of the Fourier basis. Nevertheless, ideal low-pass filtering in the cepstral domain or by other wavelet-based filtering procedures have several drawbacks.
First, they are highly supervised to adequately set the cutoff frequency parameter which is crucial and different for each ultrasound image because of the spatial variability of the PSF (due to the presence of different interrogated tissues between the transducer and the anatomical structure to be imaged).
Second, these classical filtering methods are not robust enough to give a good estimate of the PSF spectrum and often tend to produce artifacts in this estimation mainly due to the ringing effect of such ideal low pass filter in the Fourier domain or due to the blocky effect inherent to the wavelet based filtering procedure.
Accordingly, there is a need for an image restoration system and method that addresses the above-described shortcomings.